# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
	"""Download the data from Yann's website, unless it's already here."""
	if not os.path.exists(work_directory):
		os.mkdir(work_directory)
	filepath = os.path.join(work_directory, filename)
	if not os.path.exists(filepath):
		filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
		statinfo = os.stat(filepath)
		print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
	return filepath
def _read32(bytestream):
	dt = numpy.dtype(numpy.uint32).newbyteorder('>')
	return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
	"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
	print('Extracting', filename)
	with gzip.open(filename) as bytestream:
		magic = _read32(bytestream)
		if magic != 2051:
			raise ValueError(
					'Invalid magic number %d in MNIST image file: %s' %
					(magic, filename))
		num_images = _read32(bytestream)
		rows = _read32(bytestream)
		cols = _read32(bytestream)
		buf = bytestream.read(rows * cols * num_images)
		data = numpy.frombuffer(buf, dtype=numpy.uint8)
		data = data.reshape(num_images, rows, cols, 1)
		return data
def dense_to_one_hot(labels_dense, num_classes=10):
	"""Convert class labels from scalars to one-hot vectors."""
	num_labels = labels_dense.shape[0]
	index_offset = numpy.arange(num_labels) * num_classes
	labels_one_hot = numpy.zeros((num_labels, num_classes))
	labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
	return labels_one_hot
def extract_labels(filename, one_hot=False):
	"""Extract the labels into a 1D uint8 numpy array [index]."""
	print('Extracting', filename)
	with gzip.open(filename) as bytestream:
		magic = _read32(bytestream)
		if magic != 2049:
			raise ValueError(
					'Invalid magic number %d in MNIST label file: %s' %
					(magic, filename))
		num_items = _read32(bytestream)
		buf = bytestream.read(num_items)
		labels = numpy.frombuffer(buf, dtype=numpy.uint8)
		if one_hot:
			return dense_to_one_hot(labels)
		return labels
class DataSet(object):
	def __init__(self, images, labels, fake_data=False, one_hot=False):
		"""Construct a DataSet. one_hot arg is used only if fake_data is true."""
		if fake_data:
			self._num_examples = 10000
			self.one_hot = one_hot
		else:
			assert images.shape[0] == labels.shape[0], (
					'images.shape: %s labels.shape: %s' % (images.shape,
																								 labels.shape))
			self._num_examples = images.shape[0]
			# Convert shape from [num examples, rows, columns, depth]
			# to [num examples, rows*columns] (assuming depth == 1)
			assert images.shape[3] == 1
			images = images.reshape(images.shape[0],
															images.shape[1] * images.shape[2])
			# Convert from [0, 255] -> [0.0, 1.0].
			images = images.astype(numpy.float32)
			images = numpy.multiply(images, 1.0 / 255.0)
		self._images = images
		self._labels = labels
		self._epochs_completed = 0
		self._index_in_epoch = 0
	@property
	def images(self):
		return self._images
	@property
	def labels(self):
		return self._labels
	@property
	def num_examples(self):
		return self._num_examples
	@property
	def epochs_completed(self):
		return self._epochs_completed
	def next_batch(self, batch_size, fake_data=False):
		"""Return the next `batch_size` examples from this data set."""
		if fake_data:
			fake_image = [1] * 784
			if self.one_hot:
				fake_label = [1] + [0] * 9
			else:
				fake_label = 0
			return [fake_image for _ in xrange(batch_size)], [
					fake_label for _ in xrange(batch_size)]
		start = self._index_in_epoch
		self._index_in_epoch += batch_size
		if self._index_in_epoch > self._num_examples:
			# Finished epoch
			self._epochs_completed += 1
			# Shuffle the data
			perm = numpy.arange(self._num_examples)
			numpy.random.shuffle(perm)
			self._images = self._images[perm]
			self._labels = self._labels[perm]
			# Start next epoch
			start = 0
			self._index_in_epoch = batch_size
			assert batch_size <= self._num_examples
		end = self._index_in_epoch
		return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False):
	class DataSets(object):
		pass
	data_sets = DataSets()
	if fake_data:
		data_sets.train = DataSet([], [], fake_data=True, one_hot=one_hot)
		data_sets.validation = DataSet([], [], fake_data=True, one_hot=one_hot)
		data_sets.test = DataSet([], [], fake_data=True, one_hot=one_hot)
		return data_sets
	TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
	TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
	TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
	TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
	VALIDATION_SIZE = 5000
	local_file = maybe_download(TRAIN_IMAGES, train_dir)
	train_images = extract_images(local_file)
	local_file = maybe_download(TRAIN_LABELS, train_dir)
	train_labels = extract_labels(local_file, one_hot=one_hot)
	local_file = maybe_download(TEST_IMAGES, train_dir)
	test_images = extract_images(local_file)
	local_file = maybe_download(TEST_LABELS, train_dir)
	test_labels = extract_labels(local_file, one_hot=one_hot)
	validation_images = train_images[:VALIDATION_SIZE]
	validation_labels = train_labels[:VALIDATION_SIZE]
	train_images = train_images[VALIDATION_SIZE:]
	train_labels = train_labels[VALIDATION_SIZE:]
	data_sets.train = DataSet(train_images, train_labels)
	data_sets.validation = DataSet(validation_images, validation_labels)
	data_sets.test = DataSet(test_images, test_labels)
	return data_sets